{"id":"W4378554333","doi":"10.22214/ijraset.2023.52728","title":"Satellite Image Dehazing","year":2023,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Image (mathematics); Artificial intelligence; Adaptability; Image restoration; Computer vision; Net (polyhedron); Transformation (genetics); Set (abstract data type); Satellite; Haze; Image quality; Degradation (telecommunications); Deep learning; Satellite image; Image processing; Mathematics; Telecommunications; Geography; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00454714,0.00007373795,0.0000838208,0.003856213,0.0001950711,0.0004691701,0.002185495,0.00005346976,9.855221e-7],"category_scores_gemma":[0.000500044,0.00007145482,0.00001507849,0.002840046,0.0002948942,0.0004927442,0.0008076766,0.0004477215,0.00001822475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002438864,"about_ca_system_score_gemma":0.0001294137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001779911,"about_ca_topic_score_gemma":7.394683e-7,"domain_scores_codex":[0.9981009,0.000006355946,0.0001900324,0.0002941111,0.0008261026,0.0005825305],"domain_scores_gemma":[0.9991386,0.0001245519,0.00002974282,0.0001907496,0.0004465589,0.00006982521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004998062,0.00001192897,0.0000564502,0.000007443465,0.000005100687,0.0000702897,0.0001162525,0.00006196967,0.4977602,0.2918568,0.0003672215,0.2096813],"study_design_scores_gemma":[0.0007454829,0.0001660771,0.0009947047,0.0001606895,9.845016e-7,0.0003376345,0.0002169184,0.2652633,0.3251073,0.3505996,0.05605913,0.0003481459],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07845381,0.0001488525,0.8987547,0.01747202,0.001218216,0.000519264,0.000001733083,0.001118389,0.002313069],"genre_scores_gemma":[0.8598862,0.0003552889,0.1395113,0.00003263863,0.00006622943,0.0001052609,4.854829e-7,0.000008380498,0.00003418416],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7814324,"threshold_uncertainty_score":0.4524218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05793989301492493,"score_gpt":0.4108835250191862,"score_spread":0.3529436320042613,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}